autoresearch

Autonomous iteration handles task optimization by executing constrained loops that plan, verify, and refine actions. It applies mechanical verification via dry-run checks and Git-based memory to prevent regressions and guide future changes.

Yes, autonomous verification loops prevent regressions by applying mechanical verification through dry-run commands and report-ready metrics. This ensures actions are validated before execution across code optimization and security audit workflows.

Git-based memory guides future iterations by storing past task execution results and reading them to inform subsequent changes. This mechanism helps autonomous loops avoid repeating failures during code optimization and documentation workflows.

The best way to set up bounded iterations is configuring the iteration limit per need before starting autonomous learning workflows. Bounded iterations constrain the planning, verification, and refinement loop to ensure tasks complete within defined boundaries.

Yes, you need a specific verification command to start autonomous planning loops. Providing a goal, scope, and verification command initiates the constrained autoresearch loop, enabling mechanical verification through dry-run checks and results logging.

Limitations of autonomous iteration for security audits include the dependency on provided verification commands for mechanical verification. Without accurate dry-run checks and defined scopes, the bounded iterations may fail to identify vulnerabilities or prevent regressions effectively.